# Conceptual Architecture for a Probabilistic Media Trust Assessment Engine

class MediaAsset:
    """Represents an incoming media asset (image, video frame, audio segment)."""
    def __init__(self, content_id: str, data_payload: bytes, metadata: dict):
        self.content_id = content_id # Unique identifier
        self.data_payload = data_payload # Raw media bytes
        self.metadata = metadata # Source, timestamp, creator, etc.

class TrustScoreReport:
    """Encapsulates the aggregated probabilistic trust score and contributing factors."""
    def __init__(self, overall_score: float, factor_scores: dict):
        self.overall_score = overall_score  # A float from 0.0 (highly dubious) to 1.0 (highly trustworthy)
        self.factor_scores = factor_scores # e.g., {'visual_consistency': 0.8, 'audio_integrity': 0.6}
        self.explanations = {} # Human-readable insights based on factor_scores

class ProbabilisticFactChecker:
    """The central engine for assessing the probabilistic trust of media assets."""

    def __init__(self):
        # Initialize a suite of specialized, independent evaluation modules.
        # Each module is designed to identify specific types of anomalies or inconsistencies
        # and report its findings as a probability score.
        self.evaluation_modules = [
            VisualAnomalyDetector(),        # e.g., assesses pixel-level inconsistencies, lighting physics
            AudioForensicsAnalyzer(),       # e.g., detects audio spectrum anomalies, voice cloning artifacts
            SemanticConsistencyChecker(),   # e.g., evaluates contextual logic, object interactions
            SourceProvenanceTracker(),      # e.g., verifies origin, chain of custody, historical integrity
            BehaviouralPatternAnalyzer()    # e.g., flags unnatural movements or expressions in video
        ]

    def assess_media_trust(self, media_asset: MediaAsset) -> TrustScoreReport:
        """
        Processes a media asset through multiple evaluators and aggregates their scores.
        """
        individual_probabilities = {}
        for module in self.evaluation_modules:
            # Each module runs its analysis and returns a confidence score (probability)
            # indicating the likelihood of the media being authentic within its domain.
            module_score = module.evaluate(media_asset)
            individual_probabilities[module.__class__.__name__] = module_score

        # Aggregate the individual probabilities into a single, overall trust score.
        # This aggregation is a sophisticated step, potentially involving Bayesian networks,
        # weighted averages, or machine learning models trained on ground truth data.
        overall_trust = self._aggregate_scores(individual_probabilities, media_asset.metadata)

        # Generate explanations for user transparency (e.g., "Visuals show minor inconsistencies," "Source is unverified.")
        explanations = self._generate_explanations(individual_probabilities)

        return TrustScoreReport(overall_trust, individual_probabilities, explanations)

    def _aggregate_scores(self, scores: dict, metadata: dict) -> float:
        """
        A placeholder for the complex aggregation logic.
        This would consider the context, metadata, and interdependencies of scores.
        """
        if not scores:
            return 0.5 # Neutral if no data
        # Example: Simple average (in reality, much more complex with weights and contextual logic)
        return sum(scores.values()) / len(scores)

    def _generate_explanations(self, scores: dict) -> dict:
        """Translates numerical scores into human-readable insights."""
        explanations = {}
        for factor, score in scores.items():
            if score < 0.4:
                explanations[factor] = f"{factor.replace('Checker', '').replace('Analyzer', '').replace('Detector', '').strip()} indicates significant irregularities."
            elif score < 0.7:
                explanations[factor] = f"{factor.replace('Checker', '').replace('Analyzer', '').replace('Detector', '').strip()} shows minor inconsistencies."
            else:
                explanations[factor] = f"{factor.replace('Checker', '').replace('Analyzer', '').replace('Detector', '').strip()} appears consistent."
        return explanations

# --- Example Usage ---
if __name__ == "__main__":
    # Simulate receiving a potentially dubious media asset
    dubious_image_data = b"..." # Imagine raw image bytes of an unverified image
    image_metadata = {"source_url": "unknown-forum.net/post123", "creation_timestamp": "2023-10-27T14:30:00Z", "publisher": "Anonymous"}
    dubious_media = MediaAsset("img_001", dubious_image_data, image_metadata)

    fact_checker = ProbabilisticFactChecker()
    trust_report = fact_checker.assess_media_trust(dubious_media)

    print(f"Content ID: {trust_report.content_id}")
    print(f"Overall Media Trust Score: {trust_report.overall_score:.2f}")
    print("\nContributing Factors & Insights:")
    for factor, score in trust_report.factor_scores.items():
        print(f"  - {factor}: {score:.2f} ({trust_report.explanations.get(factor, '')})")

    if trust_report.overall_score < 0.3:
        print("\n**WARNING**: This media asset is highly dubious. Exercise extreme skepticism.")
    elif trust_report.overall_score < 0.6:
        print("\n**CAUTION**: This media asset has questionable elements. Independent verification is strongly recommended.")
    else:
        print("\nNOTE: This media asset appears reasonably trustworthy based on current analysis.")